Titlebook: Advances in Intelligent Tutoring Systems; Roger Nkambou,Jacqueline Bourdeau,Riichiro Mizoguc Book 2010 Springer-Verlag Berlin Heidelberg 2

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Climate Change Impact on Water Structures,utoring systems (ITS) to simulate this interaction. We have previously modeled the strategies, actions, and dialogue of novice tutors in an ITS, called AutoTutor, with learning gains comparable to novice tutors. There is evidence, however, that expert human tutors may foster exceptional learning gai

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Selin Deliceirmak,Ilkay Salihoglu more than 80%accuracy compared to student self-reports, using wireless sensors that provide data about posture, movement, grip tension, facially expressed mental states and arousal. Pedagogical agents have been used that provide emotional or motivational feedback. Students using such agents increas

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Keiichi Mochida,Lam–Son Phan Tranor performance. It provides examples of how to represent domain content and describes evaluation methodologies. Several future scenarios for student models are discussed. For example, we envision that student models will support assessment for both formative issues (the degree to which the student h

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Ashwani Kumar,Yuan-Yeu Yau,Renate Scheibe, the process of having an ITS build a model of relevant student’s traits/states during interaction, is a task permeated with uncertainty, which naturally calls for probabilistic approaches. In this chapter, I will describe techniques and issues involved in building probabilistic student models base

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https://doi.org/10.1007/978-981-15-5228-1ble interface is created for use by learners, and in some cases for others who aid their learning, including peers, parents and teachers. The chapter describes the range of purposes that Open Learner Models can serve, illustrating these with diverse examples of the ways that they have been made avai

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